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Web Usage Mining in Noisy and Ambiguous Environments: Exploring the Role of Concept Hierarchies, Compression, and Robust User Profiles

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From Web to Social Web: Discovering and Deploying User and Content Profiles (WebMine 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4737))

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Abstract

Recent efforts in Web usage mining have started incorporating more semantics into the data in order to obtain a representation deeper than shallow clicks. In this paper, we review these approaches, and examine the incorporation of simple cues from a website hierarchy in order to relate clickstream events that would otherwise seem unrelated, and thus perform URL compression. We study their effect on data reduction and on the quality of the resulting knowledge discovery. Web usage data is also notorious for containing moderate to high amounts of noise, thus motivating the use of robust knowledge discovery algorithms that can resist noise and outliers with various degrees of resistance or robustness. Therefore, we also examine the effect of robustness on the final quality of the knowledge discovery. Our experimental results conclude that post-processed and robust user profiles have better quality than raw profiles that are estimated through optimization alone. However URL compression, as expected, tends to reduce the quality, but also can drastically reduce the size of the data set, resulting in faster mining.

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Authors and Affiliations

  1. Knowledge Discovery & Web Mining Lab, University of Louisville, Louisville, KY 40292, USA

    Olfa Nasraoui & Esin Saka

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  1. Olfa Nasraoui
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  2. Esin Saka
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Bettina Berendt Andreas Hotho Dunja Mladenic Giovanni Semeraro

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Nasraoui, O., Saka, E. (2007). Web Usage Mining in Noisy and Ambiguous Environments: Exploring the Role of Concept Hierarchies, Compression, and Robust User Profiles. In: Berendt, B., Hotho, A., Mladenic, D., Semeraro, G. (eds) From Web to Social Web: Discovering and Deploying User and Content Profiles. WebMine 2006. Lecture Notes in Computer Science(), vol 4737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74951-6_5

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  • DOI: https://doi.org/10.1007/978-3-540-74951-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74950-9

  • Online ISBN: 978-3-540-74951-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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